I have two dataframes like this:
DATE MAX_AMOUNT MIN_AMOUNT MAX_DAY MIN_DAY RATE 01/09/2022 20 15 10 5 0.01 01/09/2022 25 20 15 10 0.02 03/09/2022 30 10 5 3 0.03 03/09/2022 40 30 20 5 0.04 04/09/2022 10 5 10 1 0.05 ID DATE AMOUNT DAY 1 01/09/2022 18 7 2 01/09/2022 22 11 3 01/09/2022 30 20 4 03/09/2022 35 10 5 04/09/2022 35 10
I want to bring the RATE values to the second df in accordance with the DATE. Also, the AMOUNT and DAY values in the relevant DATE must be within the appropriate range (MAX_AMOUNT & MIN_AMOUNT, MAX_DAY & MIN_DAY).
Desired output like this:
ID DATE AMOUNT DAY RATE 1 01/09/2022 18 7 0.01 2 01/09/2022 22 11 0.02 3 01/09/2022 30 20 4 03/09/2022 35 10 0.04 5 04/09/2022 35 10
Could you please help me about this?
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Answer
Use merge
first with filter columns by Series.between
and then use Series.map
for RATE
column with first matched ID
– added DataFrame.drop_duplicates
:
df = df2.merge(df1, on='DATE') df = (df[df['AMOUNT'].between(df['MIN_AMOUNT'], df['MAX_AMOUNT']) & df['DAY'].between(df['MIN_DAY'], df['MAX_DAY'])]) df2['RATE'] = df2['ID'].map(df.drop_duplicates('ID').set_index('ID')['RATE']) print (df2) ID DATE AMOUNT DAY RATE 0 1 01/09/2022 18 7 0.01 1 2 01/09/2022 22 11 0.02 2 3 01/09/2022 30 20 NaN 3 4 03/09/2022 35 10 0.04 4 5 04/09/2022 35 10 NaN